{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# high-dimensional reacher\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def smooth(y, radius=10, mode='two_sided'):\n",
    "    if len(y) < 2*radius+1:\n",
    "        return np.ones_like(y) * y.mean()\n",
    "    elif mode == 'two_sided':\n",
    "        convkernel = np.ones(2 * radius+1)\n",
    "        return np.convolve(y, convkernel, mode='same') / \\\n",
    "               np.convolve(np.ones_like(y), convkernel, mode='same')\n",
    "    elif mode == 'causal':\n",
    "        convkernel = np.ones(radius)\n",
    "        out = np.convolve(y, convkernel,mode='full') / \\\n",
    "              np.convolve(np.ones_like(y), convkernel, mode='full')\n",
    "        return out[:-radius+1]\n",
    "\n",
    "\n",
    "def plot(x, data, color, label):\n",
    "    y_m=np.mean(data, axis=0)\n",
    "    y_std=np.std(data, axis=0)\n",
    "    y_upper=y_m+y_std\n",
    "    y_lower=y_m-y_std\n",
    "    plt.fill_between(\n",
    "    x, list(y_lower), list(y_upper), interpolate=True, facecolor=color, linewidth=0.0, alpha=0.3\n",
    ")\n",
    "    plt.plot(x, list(y_m), color=color, label=label)\n",
    "    \n",
    "plt.figure(figsize=(8,6))\n",
    "file_pre = './'\n",
    "y=np.load(file_pre+'rewards_sac_lstm.npy')\n",
    "y_=np.load(file_pre+'rewards_sac.npy')\n",
    "\n",
    "x=np.arange(len(y))\n",
    "x_=np.arange(len(y_))\n",
    "batch_size=300\n",
    "episode_length = 150\n",
    "x = episode_length*x*batch_size\n",
    "x_ = episode_length*x_*batch_size\n",
    "plt.plot(x, smooth(y), label = 'SAC_LSTM', color='b')\n",
    "plt.plot(x_, smooth(y_), label = 'SAC', color='r')\n",
    "\n",
    "plt.xlabel('Samples')\n",
    "plt.ylabel('Reward')\n",
    "# plt.ylim(0)\n",
    "leg= plt.legend( loc=2)\n",
    "plt.grid()\n",
    "plt.savefig('reward_compare_sac.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "980\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# high-dimensional reacher\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def smooth(y, radius=10, mode='two_sided'):\n",
    "    if len(y) < 2*radius+1:\n",
    "        return np.ones_like(y) * y.mean()\n",
    "    elif mode == 'two_sided':\n",
    "        convkernel = np.ones(2 * radius+1)\n",
    "        return np.convolve(y, convkernel, mode='same') / \\\n",
    "               np.convolve(np.ones_like(y), convkernel, mode='same')\n",
    "    elif mode == 'causal':\n",
    "        convkernel = np.ones(radius)\n",
    "        out = np.convolve(y, convkernel,mode='full') / \\\n",
    "              np.convolve(np.ones_like(y), convkernel, mode='full')\n",
    "        return out[:-radius+1]\n",
    "\n",
    "\n",
    "def plot(x, data, color, label):\n",
    "    y_m=np.mean(data, axis=0)\n",
    "    y_std=np.std(data, axis=0)\n",
    "    y_upper=y_m+y_std\n",
    "    y_lower=y_m-y_std\n",
    "    plt.fill_between(\n",
    "    x, list(y_lower), list(y_upper), interpolate=True, facecolor=color, linewidth=0.0, alpha=0.3\n",
    ")\n",
    "    plt.plot(x, list(y_m), color=color, label=label)\n",
    "    \n",
    "plt.figure(figsize=(8,6))\n",
    "file_pre = './'\n",
    "y=np.load(file_pre+'rewards_td3_lstm.npy')\n",
    "y_=np.load(file_pre+'rewards_td3.npy')\n",
    "\n",
    "x=np.arange(len(y))\n",
    "x_=np.arange(len(y_))\n",
    "print(len(y_))\n",
    "batch_size=300\n",
    "episode_length = 150\n",
    "x = episode_length*x*batch_size\n",
    "x_ = episode_length*x_*batch_size\n",
    "plt.plot(x, smooth(y), label = 'TD3_LSTM', color='b')\n",
    "plt.plot(x_, smooth(y_), label = 'TD3', color='r')\n",
    "\n",
    "plt.xlabel('Samples')\n",
    "plt.ylabel('Reward')\n",
    "# plt.ylim(0)\n",
    "leg= plt.legend( loc=2)\n",
    "plt.grid()\n",
    "plt.savefig('reward_compare_td3.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
